Search Results (816)

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

Modeling mainshock–aftershock ground motions is essential for seismic risk assessment, especially in regions experiencing frequent earthquakes. Recent studies have often employed Copula-based joint distributions or machine learning techniques to simulate the statistical dependency between mainshock and aftershock parameters. While effective at capturing nonlinear correlations, these methods are typically black box in nature, data-dependent, and difficult to generalize across tectonic settings. More importantly, they tend to focus solely on marginal or joint parameter correlations, which implicitly treat mainshocks and aftershocks as independent stochastic processes, thereby overlooking their inherent spectral interaction. To address these limitations, this study proposes an explicit and parameterized modeling framework based on the evolutionary power spectral density (EPSD) of random ground motions. Using the magnitude difference between a mainshock and an aftershock as the control variable, we derive attenuation relationships for the amplitude, frequency content, and duration. A coherence function model is further developed from real seismic records, treating the mainshock–aftershock pair as a vector-valued stochastic process and thus enabling a more accurate representation of their spectral dependence. Coherence analysis shows that the function remains relatively stable between 0.3 and 0.6 across the 0–30 Rad/s frequency range. Validation results indicate that the simulated response spectra align closely with recorded spectra, achieving R² values exceeding 0.90 and 0.91. To demonstrate the model’s applicability, a case study is conducted on a representative frame structure to evaluate seismic vulnerability and economic loss. As the mainshock PGA increases from 0.2 g to 1.2 g, the structure progresses from slight damage to complete collapse, with loss rates saturating near 1.0 g. These findings underscore the engineering importance of incorporating mainshock–aftershock spectral interaction in seismic damage and risk modeling, offering a transparent and transferable tool for future seismic resilience assessments. Full article

Medical Visual Question Answering (MedVQA) lies at the intersection of computer vision, natural language processing, and clinical decision-making, aiming to generate accurate responses from medical images paired with complex inquiries. Despite recent advances in vision–language models (VLMs), their use in healthcare remains limited by a lack of interpretability and a tendency to produce direct, unexplainable outputs. This opacity undermines their reliability in medical settings, where transparency and justification are critically important. To address this limitation, we propose a zero-shot chain-of-thought prompting framework that guides VLMs to perform multi-step reasoning before arriving at an answer. By encouraging the model to break down the problem, analyze both visual and contextual cues, and construct a stepwise explanation, the approach makes the reasoning process explicit and clinically meaningful. We evaluate the framework on the PMC-VQA benchmark, which includes authentic radiological images and expert-level prompts. In a comparative analysis of three leading VLMs, Gemini 2.5 Pro achieved the highest accuracy (72.48%), followed by Claude 3.5 Sonnet (69.00%) and GPT-4o Mini (67.33%). The results demonstrate that chain-of-thought prompting significantly improves both reasoning transparency and performance in MedVQA tasks. Full article

As yet, no study has investigated the use of a research magnetic resonance imaging (MRI) scanner to support undergraduate medical radiation science (MRS) students in developing their MRI knowledge and practical skills (competences). The purpose of this study was to test an innovative program for a total of 10 s- and third-year students of a MRS course to enhance their MRI competences. The study involved an experimental, two-week MRI learning program which focused on practical MRI scanning of phantoms and healthy volunteers. Pre- and post-program questionnaires and tests were used to evaluate the competence development of these participants as well as the program’s educational quality. Descriptive statistics, along with Wilcoxon signed-rank and paired t-tests, were used for statistical analysis. The program improved the participants’ self-perceived and actual MRI competences significantly (from an average of 2.80 to 3.20 out of 5.00, p = 0.046; and from an average of 34.87% to 62.72%, Cohen’s d effect size: 2.53, p < 0.001, respectively). Furthermore, they rated all aspects of the program’s educational quality highly (mean: 3.90–4.80 out of 5.00) and indicated that the program was extremely valuable, very effective, and practical. Nonetheless, further evaluation should be conducted in a broader setting with a larger sample size to validate the findings of this feasibility study, given the study’s small sample size and participant selection bias. Full article

Geometric morphometrics is performed on different species in different contexts. Here, the aim was to investigate morphological differences in the head of the Sanmartinero Creole bovine to examine head shape variations between sexes using geometric morphometric methods. A sample of cranial pictures of 43 animals (13 males and 30 females) was obtained, and form (size + shape) was studied by means of geometric morphometric techniques using a set of 14 landmarks. This approach eliminated potential dietary effects, ensuring that the observed shape variations were primarily due to intrinsic morphological differences. Sexual dimorphism was found in form (for both size and shape) of the head of the Sanmartinero Creole bovine breed. Males had significantly larger heads based on centroid size (U = 714, p = 0.0004), confirming true sexual size differences, and Principal Component Analysis revealed overlapping head shapes with sexual dimorphism concentrated at midline sagittal landmarks (between the most rostral and caudal orbit points) and paired lateral points, indicating that males have broader and longer heads. The two evaluated characters (head size and shape) are of special interest for the conservation of the breed, especially in those cases whose objectives are to maintain the uniqueness, distinctiveness, and uniformity of the populations. This study analyzed animals subjected to the same feeding program, ensuring the elimination of additional variables. Full article

Satellite-derived bathymetry (SDB) enables the efficient mapping of shallow waters such as coastal zones but typically requires extensive local ground truth data to achieve high accuracy. This study evaluates the effectiveness of transfer learning in reducing this requirement while keeping estimation accuracy at acceptable levels by adapting a deep learning model pretrained on data from Puck Lagoon (Poland) to a new coastal site in Agia Napa (Cyprus). Leveraging the open MagicBathyNet benchmark dataset and a lightweight U-Net architecture, three scenarios were studied and compared: direct inference to Cyprus, site-specific training in Cyprus, and fine-tuning from Poland to Cyprus with incrementally larger subsets of training data. Results demonstrate that fine-tuning with 15 samples reduces RMSE by over 50% relative to the direct inference baseline. In addition, the domain adaptation approach using 15 samples shows comparable performance to the site-specific model trained on all available data in Cyprus. Depth-stratified error analysis and paired statistical tests confirm that around 15 samples represent a practical lower bound for stable SDB, according to the MagicBathyNet benchmark. The findings of this work provide quantitative evidence on the effectiveness of deploying data-efficient SDB pipelines in settings of limited in situ surveys, as well as a practical lower bound for clear and shallow coastal waters. Full article

Objectives: The objective of this study was to evaluate the agreement and clinical interchangeability of intraocular pressure (IOP) measurements obtained with the mechanical Haag-Streit AT900 Goldmann applanation tonometer (mGAT) and the digital Huvitz HT5000 applanation tonometer (dGAT). Methods: This retrospective comparative study included 53 eyes of 28 patients undergoing routine ophthalmologic evaluation. Each eye underwent IOP measurement using both mGAT and dGAT in a randomized sequence. Central corneal thickness (CCT) was also recorded. Pearson’s correlation coefficient was used to determine correlation between paired IOP measurements. Bland–Altman plots were graphed for the analysis of differences for IOP between the instruments. Results: A total of 53 eyes of 28 patients (15 males) were included in the study. The mean age of the patients was 62.6 years. The mean mGAT and dGAT measurements were 16.3 ± 6.6 mmHg (range 9–50) and 16.4 ± 6.2 mmHg (range 8.8–45.9), respectively (p = 0.53). A strong, significant positive correlation was found for paired IOP measurements by the two instruments (r = 0.98; p < 0.0001). Bland–Altman analysis revealed 95% limits of agreement from −2.5 to +2.3 mmHg, with a small but statistically significant proportional bias favoring mGAT at higher IOP levels. Additionally, 91% of paired measurements were within ±2 mmHg. CCT-related differences were statistically and clinically insignificant. Conclusions: IOP measurements obtained with mGAT and dGAT were highly correlated and clinically interchangeable for the range tested. The Huvitz HT5000 may serve as a reliable alternative to the classic Goldmann tonometer in routine clinical settings. Full article

Aeromonas spp. are opportunistic pathogens that are widely distributed in water sources, with several species being associated with fish and human diseases. We have previously identified an Aeromonas AB005 isolate from diseased Acipencer baerii. This isolate was identified as A. hydrophila based on the 16S rRNA and gyrB gene sequences. However, this novel strain does not produce indole and tested negative for ornithine decarboxylase and d-xylose fermentation—differences that set it apart from typical A. hydrophila strains. In the present study, this strain was subjected to whole-genome sequencing and compared with the genomes of the type strain (Aeromonas hydrophila ATCC 7966^T) and other Aeromonas spp. Comprehensive genome analysis suggests that AB005 represents a distinct species within the genus. The draft genome of the AB005 strain comprises 4,780,815 base pairs with a GC content of 61.2% and contains 6104 predicted protein-coding sequences along with numerous genes implicated in antibiotic resistance. The core/pan-genome analysis reveals extensive genetic diversity, indicative of a dynamic genomic structure. These findings collectively underscore the taxonomic distinction of the AB005 strain as a novel species and highlight its potential pathogenic implications in aquaculture and public health settings. Full article

Background/Objectives: Postoperative deep vein thrombosis (DVT) is a common and serious complication after tibial fracture surgery. This study aimed to develop and evaluate machine learning (ML) models to predict the occurrence of DVT following tibia fracture surgery. Methods: A retrospective analysis was conducted on patients who had undergone surgery for isolated tibial fractures. A total of 42 predictive models were developed using combinations of six ML algorithms—logistic regression, support vector machine, random forest, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), and neural networks—and seven feature selection methods, including SHapley Additive exPlanations (SHAP), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, recursive feature elimination, univariate filtering, and full-variable inclusion. Model performance was assessed based on discrimination, quantified by the area under the receiver operating characteristic curve (AUC-ROC), and calibration, measured using Brier scores, with internal validation performed via bootstrapping. Results: Of 471 patients, 80 (17.0%) developed postoperative DVT. The ML models achieved high overall accuracy in predicting DVT. Twenty-four models showed similarly excellent discrimination (pairwise AUC comparisons, p > 0.05). The top-performing model (random forest with RFE) attained an AUC of ~0.99, while several others (including LightGBM and SVM-based models) also reached AUC values in the 0.97–0.99 range. Notably, support vector machine models paired with Boruta or LASSO feature selection demonstrated the best calibration (lowest Brier scores), indicating reliable risk estimation. The final selected SVM models achieved high specificity (≥95%) with moderate sensitivity (~75–80%) for DVT detection. Conclusions: ML models demonstrated high accuracy in predicting postoperative DVT following tibial fracture surgery. Support vector machine-based models showed particularly favorable discrimination and calibration. These results suggest the potential utility of ML-based risk stratification to guide individualized prophylaxis, warranting further validation in prospective clinical settings. Full article

Glaucoma is a leading cause of irreversible blindness worldwide, yet early detection can prevent vision loss. This paper proposes a novel deep learning approach that combines two ophthalmic imaging modalities, fundus photographs and optical coherence tomography scans, as paired images from the same eye of each patient for automated glaucoma detection. We develop separate convolutional neural network models for fundus and optical coherence tomography images and a fusion model that integrates features from both modalities for each eye. The models are trained and evaluated on a private clinical dataset (Bangladesh Eye Hospital and Institute Ltd.) consisting of 216 healthy eye images (108 fundus, 108 optical coherence tomography) from 108 patients and 200 glaucomatous eye images (100 fundus, 100 optical coherence tomography) from 100 patients. Our methodology includes image preprocessing pipelines for each modality, custom convolutional neural network/ResNet-based architectures for single-modality analysis, and a two-branch fusion network combining fundus and optical coherence tomography feature representations. We report the performance (accuracy, sensitivity, specificity, and area under curve) of the fundus-only, optical coherence tomography-only, and fusion models. In addition to a fixed test set evaluation, we perform five-fold cross-validation, confirming the robustness and consistency of the fusion model across multiple data partitions. On our fixed test set, the fundus-only model achieves 86% accuracy (AUC 0.89) and the optical coherence tomography-only model, 84% accuracy (AUC 0.87). Our fused model reaches 92% accuracy (AUC 0.95), an absolute improvement of 6 percentage points and 8 percentage points over the fundus and OCT baselines, respectively. McNemar’s test on pooled five-fold validation predictions (b = 3, c = 18) yields ${\chi }^2=10.7$ (p = 0.001), and on optical coherence tomography-only vs. fused (b_o = 5, c_o = 20) ${\chi }_o^2=9.0$ (p = 0.003), confirming that the fusion gains are significant. Five-fold cross-validation further confirms these improvements (mean AUC $0.952\pm 0.011$. We also compare our results with the existing literature and discuss the clinical significance, limitations, and future work. To the best of our knowledge, this is the first time a novel deep learning model has been used on a fusion of paired fundus and optical coherence tomography images of the same patient for the detection of glaucoma. Full article

The maritime industry remains a significant contributor to global greenhouse gas (GHG) emissions. In this article, a systematic study has been performed on the alternative fuel emissions of large cargo ships under different route scenarios and propulsion systems. For this purpose, a set of key performance indicators (KPIs) are evaluated, including total equivalent CO₂ emissions (CO_2eq), CO_2eq emissions per unit of transport mass and CO_2eq emissions per unit of transport mass per distance. The emissions analysis demonstrates that Liquified Natural Gas (LNG) paired with Marine Gas Oil (MGO) emerges as the most viable short-term solution in comparison with the conventional fuel oil propulsion. Synthetic methanol (eMeOH) paired with synthetic diesel (eDiesel) is identified as the most promising long-term fuel combination. When comparing the European Union (EU) emission calculation system (FuelEU) with the International Maritime Organization (IMO) emission metrics, a discrepancy in emissions reduction outcomes has been observed. The IMO approach appears to favor methanol (MeOH) and liquefied natural gas (LNG) over conventional fuel oil. This is attributed to the fact that the IMO metrics do not consider unburned methane emissions (methane slip) and emissions in the production of fuels (Well-to-Tank). Full article

In arid agricultural production, exploring suitable water-saving irrigation strategies and analyzing their water-saving mechanisms are of great significance. Alternating partial root-zone drying irrigation (APRI), a water-saving strategy, enhances the water use efficiency (WUE) of alfalfa (Medicago sativa L.) This paper aims to clarify the physiological mechanisms by which the APRI method enhances the physiological WUE of alfalfa, as well as the differences between this water-saving irrigation strategy, conventional irrigation (CI), and their water deficit adjustments, in order to seek higher water use efficiency for alfalfa production in arid regions. In this experiment, alfalfa was used as the research subject, and three irrigation methods, CI, fixed partial root-zone drying (FPRI), and APRI, were set up, each paired with three decreasing moisture supply gradients of 90% water holding capacity (WHC) (W1), 70% WHC (W2), and 50% WHC (W3). Samples were taken and observed once after every three complete irrigation cycles. Through a comparative analysis of the growth status, leaf water status, antioxidant enzyme activity, and osmotic adjustment capabilities of alfalfa under different water supplies for the three irrigation strategies, the following conclusions were drawn: First, the APRI method, through artificially created periodic wet–dry cycles in the rhizosphere soil, provides pseudo-drought stress that enhances the osmotic adjustment capabilities and antioxidant enzyme activity of alfalfa leaves during the early to middle phases of irrigation treatment compared to CI and FPRI methods, resulting in healthier leaf water conditions. Secondly, the stronger drought tolerance and superior growth conditions of alfalfa under the APRI method due to reduced water availability are key factors in enhancing the water use efficiency of alfalfa under this strategy. Full article

Background: This study aimed to evaluate the long-term stability of maxillary molar distalization in the treatment of Class II malocclusion. Methods: This study included 40 patients (31 males and 9 females) who received fixed orthodontic treatment after maxillary molar distalization. Orthodontic models and lateral cephalograms were evaluated at three time points: pre-treatment (T1), after orthodontic treatment (T2), and long-term follow-up (T3). The mean ages of the patients’ ages at T1, T2, and T3 were 13.02, 15.97, and 22.05 years, respectively. The statistical analysis included paired t-tests and Wilcoxon signed-rank tests. The statistical significance was set at p < 0.05. Results: The statistical analysis indicated no gender-related differences. A significant distalization of maxillary first molars was observed at T2 compared to T1 (p < 0.001). Despite a minor relapse, a statistically significant distalization was observed in T3-T1 (p < 0.001). The vertical skeletal angles, which increased during the treatment period, decreased at T3-T2. The molar relationship was almost maintained after long-term follow-up (p < 0.001). Conclusions: The maxillary molar distalization achieved in the Class II treatment was maintained in the long term. The vertical skeletal measurements decreased to their initial values in the long term. The Class I molar relationship did not change during the completion of the growth. This study hypothesized that the maxillary molar distalization achieved during fixed orthodontic treatment can be maintained in the long term without significant relapse. Full article

Background/Objectives: Cognitive deficiency associated with chronic alcohol consumption in older people remains an under-investigated public health issue in Romania, particularly concerning rural–urban disparities and the impact of reversible hepatic dysfunction on cognitive performance. To evaluate cognitive function at hospital admission and discharge using the Mini-Mental State Examination (MMSE); to identify rural–urban disparities; and to analyze the relationship between hepatic markers and MMSE scores in older people with chronic alcohol consumption. Methods: This retrospective, single-center observational study was conducted on 152 patients aged ≥55 years, hospitalized between January 2021 and December 2023 at the “Elisabeta Doamna” Psychiatric Hospital, Galați. Demographic variables, MMSE scores (at admission and discharge), and hepatic parameters (AST, ALT, GGT, total bilirubin, and ammonia) were collected. Statistical analysis included descriptive statistics, chi-square tests for categorical variables, paired t-tests or ANOVA for MMSE scores, and Pearson correlations between MMSE and hepatic markers (α = 0.05). Results: At admission, 94% of patients had an MMSE score < 24. The mean MMSE score increased from 23.4 ± 4.1 to 25.0 ± 3.7 at discharge (Δ = +1.6; p < 0.001). Patients from rural areas (63.8% of the sample) had significantly lower MMSE scores at admission compared to urban patients (22.6 ± 3.9 vs. 24.8 ± 4.2; p = 0.02). However, no statistically significant difference was observed between rural and urban patients regarding cognitive improvement during hospitalization (p = 0.88), indicating that the initial gap persisted at discharge. GGT levels were inversely correlated with MMSE scores (r = −0.41; p < 0.001), suggesting a contribution of hepatic dysfunction to cognitive decline. Conclusions: Alcohol-related cognitive impairment is highly prevalent among older patients hospitalized for withdrawal, with partial reversibility observed through inpatient management. The observed rural disparities and the association between hepatic dysfunction and cognitive performance highlight the need of concurrent MMSE and hepatic screening, with prioritized interventions in rural settings. Prospective, multicenter studies are warranted to validate these findings and to identify additional prognostic biomarkers. Full article

The dynamic vision sensor (DVS), or event camera, is emerging as a successful sensing solution for many application fields. While state-of-the-art datasets for event-based vision are well-structured and suitable for the designed goals, they often rely on simulated data or are recorded in loosely controlled conditions, thereby making it challenging to understand the sensor response to varying camera parameters and illumination conditions. To address this knowledge gap, this work introduces the JRC INVISIONS Neuromorphic Sensors Parametric Tests dataset, an extensive collection of event-based data specifically acquired in controlled scenarios that systematically vary bias settings and environmental factors, enabling rigorous evaluation of sensor performance, robustness, and artifacts under realistic conditions that existing datasets lack. The dataset is composed of 2156 scenes recorded with two different off-the-shelf event cameras, eventually paired with a frame camera across three different controlled scenarios: moving targets, mechanical vibrations, and rotation speed estimation; the inclusion of ground truth enables the evaluation of standard computer vision tasks. The proposed manuscript is complemented by an experimental analysis of sensor performance under varying speeds and illumination, event statistics, and acquisition artifacts such as event loss and motion-induced distortions due to line-based readout. The dataset is publicly available and, to the best of our knowledge, represents the first dataset of its kind in the literature, providing a valuable resource for the research community to advance the development of event-based vision systems and applications. Full article